Update app.py

app.py

@@ -1,201 +1,3 @@
-# import os
-# import gradio as gr
-# import requests
-# import inspect
-# import pandas as pd
-
-# # (Keep Constants as is)
-# # --- Constants ---
-# DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# # --- Basic Agent Definition ---
-# # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-# class BasicAgent:
-#     def __init__(self):
-#         print("BasicAgent initialized.")
-#     def __call__(self, question: str) -> str:
-#         print(f"Agent received question (first 50 chars): {question[:50]}...")
-#         fixed_answer = "This is a default answer."
-#         print(f"Agent returning fixed answer: {fixed_answer}")
-#         return fixed_answer
-
-# def run_and_submit_all( profile: gr.OAuthProfile | None):
-#     """
-#     Fetches all questions, runs the BasicAgent on them, submits all answers,
-#     and displays the results.
-#     """
-#     # --- Determine HF Space Runtime URL and Repo URL ---
-#     space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
-#     if profile:
-#         username= f"{profile.username}"
-#         print(f"User logged in: {username}")
-#     else:
-#         print("User not logged in.")
-#         return "Please Login to Hugging Face with the button.", None
-
-#     api_url = DEFAULT_API_URL
-#     questions_url = f"{api_url}/questions"
-#     submit_url = f"{api_url}/submit"
-
-#     # 1. Instantiate Agent ( modify this part to create your agent)
-#     try:
-#         agent = BasicAgent()
-#     except Exception as e:
-#         print(f"Error instantiating agent: {e}")
-#         return f"Error initializing agent: {e}", None
-#     # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
-#     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-#     print(agent_code)
-
-#     # 2. Fetch Questions
-#     print(f"Fetching questions from: {questions_url}")
-#     try:
-#         response = requests.get(questions_url, timeout=15)
-#         response.raise_for_status()
-#         questions_data = response.json()
-#         if not questions_data:
-#              print("Fetched questions list is empty.")
-#              return "Fetched questions list is empty or invalid format.", None
-#         print(f"Fetched {len(questions_data)} questions.")
-#     except requests.exceptions.RequestException as e:
-#         print(f"Error fetching questions: {e}")
-#         return f"Error fetching questions: {e}", None
-#     except requests.exceptions.JSONDecodeError as e:
-#          print(f"Error decoding JSON response from questions endpoint: {e}")
-#          print(f"Response text: {response.text[:500]}")
-#          return f"Error decoding server response for questions: {e}", None
-#     except Exception as e:
-#         print(f"An unexpected error occurred fetching questions: {e}")
-#         return f"An unexpected error occurred fetching questions: {e}", None
-
-#     # 3. Run your Agent
-#     results_log = []
-#     answers_payload = []
-#     print(f"Running agent on {len(questions_data)} questions...")
-#     for item in questions_data:
-#         task_id = item.get("task_id")
-#         question_text = item.get("question")
-#         if not task_id or question_text is None:
-#             print(f"Skipping item with missing task_id or question: {item}")
-#             continue
-#         try:
-#             submitted_answer = agent(question_text)
-#             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-#             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
-#         except Exception as e:
-#              print(f"Error running agent on task {task_id}: {e}")
-#              results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
-
-#     if not answers_payload:
-#         print("Agent did not produce any answers to submit.")
-#         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-#     # 4. Prepare Submission 
-#     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-#     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-#     print(status_update)
-
-#     # 5. Submit
-#     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-#     try:
-#         response = requests.post(submit_url, json=submission_data, timeout=60)
-#         response.raise_for_status()
-#         result_data = response.json()
-#         final_status = (
-#             f"Submission Successful!\n"
-#             f"User: {result_data.get('username')}\n"
-#             f"Overall Score: {result_data.get('score', 'N/A')}% "
-#             f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-#             f"Message: {result_data.get('message', 'No message received.')}"
-#         )
-#         print("Submission successful.")
-#         results_df = pd.DataFrame(results_log)
-#         return final_status, results_df
-#     except requests.exceptions.HTTPError as e:
-#         error_detail = f"Server responded with status {e.response.status_code}."
-#         try:
-#             error_json = e.response.json()
-#             error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-#         except requests.exceptions.JSONDecodeError:
-#             error_detail += f" Response: {e.response.text[:500]}"
-#         status_message = f"Submission Failed: {error_detail}"
-#         print(status_message)
-#         results_df = pd.DataFrame(results_log)
-#         return status_message, results_df
-#     except requests.exceptions.Timeout:
-#         status_message = "Submission Failed: The request timed out."
-#         print(status_message)
-#         results_df = pd.DataFrame(results_log)
-#         return status_message, results_df
-#     except requests.exceptions.RequestException as e:
-#         status_message = f"Submission Failed: Network error - {e}"
-#         print(status_message)
-#         results_df = pd.DataFrame(results_log)
-#         return status_message, results_df
-#     except Exception as e:
-#         status_message = f"An unexpected error occurred during submission: {e}"
-#         print(status_message)
-#         results_df = pd.DataFrame(results_log)
-#         return status_message, results_df
-
-
-# # --- Build Gradio Interface using Blocks ---
-# with gr.Blocks() as demo:
-#     gr.Markdown("# Basic Agent Evaluation Runner")
-#     gr.Markdown(
-#         """
-#         **Instructions:**
-
-#         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-#         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-#         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
-#         ---
-#         **Disclaimers:**
-#         Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-#         This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
-#         """
-#     )
-
-#     gr.LoginButton()
-
-#     run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-#     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-#     # Removed max_rows=10 from DataFrame constructor
-#     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-#     run_button.click(
-#         fn=run_and_submit_all,
-#         outputs=[status_output, results_table]
-#     )
-
-# if __name__ == "__main__":
-#     print("\n" + "-"*30 + " App Starting " + "-"*30)
-#     # Check for SPACE_HOST and SPACE_ID at startup for information
-#     space_host_startup = os.getenv("SPACE_HOST")
-#     space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-#     if space_host_startup:
-#         print(f"✅ SPACE_HOST found: {space_host_startup}")
-#         print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-#     else:
-#         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-#     if space_id_startup: # Print repo URLs if SPACE_ID is found
-#         print(f"✅ SPACE_ID found: {space_id_startup}")
-#         print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-#         print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-#     else:
-#         print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-#     print("-"*(60 + len(" App Starting ")) + "\n")
-
-#     print("Launching Gradio Interface for Basic Agent Evaluation...")
-#     demo.launch(debug=True, share=False)
-
-
 import os
 import gradio as gr
 import requests
@@ -210,7 +12,9 @@ from smolagents import (
     OpenAIServerModel,
     DuckDuckGoSearchTool,
     FinalAnswerTool,
-    PythonInterpreterTool
+    PythonInterpreterTool,
+    InferenceClientModel,
+HfApiModel
 )
 from huggingface_hub import login, InferenceClient
 
@@ -219,17 +23,6 @@ from huggingface_hub import login, InferenceClient
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-from smolagents import CodeAgent, HfApiModel
-import os
-from smolagents import HfApiModel
-
-class CustomModel(Model):
-    def generate(messages, stop_sequences=["Task"]):
-        response = client.chat_completion(messages, stop=stop_sequences, max_tokens=1024)
-        answer = response.choices[0].message
-        return answer
-
 
 
 # --- Agent Definition ---
@@ -238,25 +31,24 @@ class BasicAgent:
         print("Initializing BasicAgent with tools...")
 
         # Load OpenAI token from environment
-        openai_token = os.getenv("OPENAI_API_KEY")
+        openai_token = os.getenv("HF_TOKEN")
         if not openai_token:
-            raise ValueError("Missing OpenAI API token!")
+            raise ValueError("Missing API token!")
 
         # Initialize model and tools
 
-        model_id = "meta-llama/Llama-3.2-1B-Instruct"
-
-
-        client = InferenceClient(model=model_id)
-
-        custom_model = CustomModel(client)
-
-
-        model = OpenAIServerModel(
-            #api_base="openai",
-            api_key=openai_token,
-            model_id="gpt-4.1"
+        model = InferenceClientModel(
+            model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+            provider="together",
+            token=openai_token,
+            max_tokens=5000,
         )
+
+        # model = OpenAIServerModel(
+        #     #api_base="openai",
+        #     api_key=openai_token,
+        #     model_id="gpt-4.1"
+        # )
         search_tool = DuckDuckGoSearchTool()
         final_answer_tool = FinalAnswerTool()
         reverse_tool = ReverseTextTool()
@@ -271,7 +63,7 @@ class BasicAgent:
 
         # Build the agent
         self.agent = CodeAgent(
-            model=custom_model,
+            model=model,
             prompt_templates=prompt_templates,
             tools=[search_tool, reverse_tool, table_tool, veg_tool, python_tool, exfood_tool], #final_answer_tool
             add_base_tools=True,